Netflix Algorithm Repurposed By Scientists For Speeding Up Biological Imaging

A team of scientists decided to repurpose a 2009 Netflix algorithm for movie prediction competition and make it reliable for speeding up biological imaging. More specifically, the Netflix algorithm was employed for the development of a technique that would make it possible to acquire classical Raman spectroscopy images at higher speeds.

The scientists observed that to speed up biological imaging they had to lower the volume of Raman spectral data gathered. They came up with this method by only obtaining a chunk of the data needed for a Raman spectroscopy. The Netflix algorithm that the researchers modified for their purposes has completed the missing portions.

“Although compressive Raman approaches have been reported previously, they couldn’t be used with biological tissues because of their chemical complexity,” said Hilton de Aguiar, the study’s leader from the Ecole Normale Superieure in France. “We combined compressive imaging with fast computer algorithms that provide the kind of images clinicians use to diagnose patients, but rapidly and without laborious manual post-processing.”

Scientists sped up biological imaging by using a Netflix algorithm for movie prediction competition

The Raman imaging is not invasive, and it’s useful to determine the chemical composition of complex samples, without any prior preparation. The Raman method was handy in identifying cancer cells, among others, but it was too slow to be useful in assessing the dynamics of biological specimens.

“With the methodology we developed, we addressed these two challenges simultaneously —increasing the speed and introducing a more straightforward way to acquire useful information from the spectroscopic images,” De Aguiar added. The scientists also explained that to make the Raman spectroscopy faster, the researchers had to make it compatible with the Netflix algorithm by substituting the standard camera setups with a spatial light modulator.

“A very fast spatial light modulator made it possible to acquire images and skip data bits very quickly,” said de Aguiar. “The spatial light modulator we used is orders of magnitude less expensive and faster than other options on the market, making the overall optical setup cheap and fast.”